system

Abstract

We provide the system. [Solution] A means for receiving and analyzing information entered from a user terminal, A means for generating an answer using a generative AI model based on the analyzed information, A means for individualizing the generated response and sending it to the user terminal, A means of updating the knowledge base considering past conversation history and user profiles, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the fact that specific users such as the elderly cannot effectively utilize digital technology causes the digital divide. In addition, there are anxieties about using digital devices due to lack of knowledge and risks of incorrect operations. Furthermore, the accompanying increase in burden on family members and surrounding support providers is also a major issue. Therefore, there is a need to provide an environment where digital technology can be used more easily and with confidence.

Means for Solving the Problems

[0005] The present invention provides a means for receiving and analyzing information input from a user terminal. It constructs a means for generating responses using a generation AI model based on the analyzed information, and a means for generating and transmitting personalized responses for each user. Furthermore, by continuously updating the knowledge base considering past dialogue history and user profiles, the system provides a more accurate and rapid response. It also incorporates a means for notifying external devices of the generated responses, thereby strengthening family collaboration and supporting users' use of digital technology.

[0006] A "user terminal" refers to an electronic device capable of inputting and outputting information, and is a device in which the user directly operates the interface.

[0007] "Inputted information" refers to text and data transmitted to the system through the user's terminal.

[0008] "Means of analysis" refers to algorithms and technologies used to process input information and understand its meaning and intent.

[0009] A "generative AI model" refers to a model that uses artificial intelligence to generate output in natural language based on given data.

[0010] "Means of generating responses" refers to the processes and technologies used to create and provide appropriate responses based on analyzed information.

[0011] "Means of personalizing and transmitting to user terminals" refers to technologies for generating customized information that takes into account the user's history and profile, and providing that information to the user's terminal.

[0012] "Dialogue history" refers to communication data between users and the system that has been recorded in the past.

[0013] A "user profile" refers to information that a system holds about a user, including data on past behavior and trends.

[0014] "Methods for updating the knowledge base" refer to technologies used to incorporate new information, expand the system's knowledge framework, and improve the accuracy of responses.

[0015] "Means of notifying external devices" refers to technologies that transmit generated information to related devices or terminals other than the user. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention uses a user terminal, a server, and a generative AI model to solve digital challenges faced by users in a rapid and personalized manner. The following describes an embodiment of the system in natural language.

[0038] First, the user uses their device to type a question about their daily digital usage into the chat app. This question might be something like, "I want to know how to download a new app." When the user presses the send button, the message is sent to the server.

[0039] The server passes the received message to a natural language processing engine for analysis. This engine extracts keywords and performs contextual analysis to understand the intent of the user's question. Based on the analysis results, the server calls a generative AI model and starts the process of generating an appropriate answer.

[0040] The generative AI model generates answers to user questions based on a knowledge base. It also considers past conversation history and user profiles to prepare responses optimized for the user. For example, it might provide specific explanations such as, "The download procedure for the XX app is as follows."

[0041] The generated responses are further refined on the server and finally sent to the user's device. The user can then use this information to operate their digital devices. The server can also notify relevant external devices of this interaction and share information with family members or support providers as needed.

[0042] Thus, the system aims to improve the quality of users' digital lives by smoothly resolving their digital problems and providing information safely and reliably.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user activates their device and uses a chat app to enter a question about digital technology. For example, they might type, "How do I download a new app on my smartphone?" Once they have finished typing, they press the send button.

[0046] Step 2:

[0047] The terminal initiates communication to send the message entered by the user to the server. The data is transmitted to the server via the internet in an encrypted state.

[0048] Step 3:

[0049] The server passes the received message to a natural language processing engine. The engine analyzes the message and identifies key keywords and intent. For example, "new app" and "download" might be extracted as key keywords.

[0050] Step 4:

[0051] The server calls an AI model based on the analysis results to generate an appropriate response. The AI ​​model refers to a knowledge base and selects the information that best matches the user's intent to generate the response.

[0052] Step 5:

[0053] The server personalizes the generated responses. This process utilizes past conversation history and user profiles to adjust the responses to be more user-friendly and personalized.

[0054] Step 6:

[0055] The server sends the finalized response to the user's terminal. At this time, if there are settings to notify relevant external devices or family members of the response content, the server will share the information accordingly.

[0056] Step 7:

[0057] The user reviews the response received on their device and operates their smartphone or other device based on the information provided. For example, they can proceed with the operation by receiving specific instructions such as, "Go to the app store, search for XX, and tap download."

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In modern society, users face a variety of digital challenges, requiring rapid and personalized responses. However, traditional systems only provide single, generic answers, lacking personalized support that takes into account user characteristics and past interactions. This problem degrades the quality of users' digital lives and increases the time it takes to obtain appropriate information.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for receiving and analyzing information input from the user's operating device, means for individualizing the generated response and transmitting it to the user's operating device, means for updating the knowledge base considering past dialogue history and user characteristic information, and means for notifying relevant information in order to share information with external devices. This enables the provision of individualized responses and efficient information sharing.

[0063] A "user control device" is a digital device used by a user to input information and receive output from a system.

[0064] "Information reception and analysis" refers to the process by which a server receives data sent by a user and understands its meaning and intent using natural language processing.

[0065] A "generative AI model" is an artificial intelligence model that generates appropriate responses based on input information while referencing a knowledge base.

[0066] "Personalized responses" refer to information provided to each user that is customized by a generative AI model.

[0067] A "knowledge base" is a collection of information that a system uses to generate answers to user questions, and includes past dialogue history and user characteristic information.

[0068] An "external device" is a device that operates in conjunction with the user's operating device or server and is used to provide additional functions or share information.

[0069] "Related information notification" is a function that sends generated responses or information processed within the system to external devices to facilitate necessary collaboration and information sharing.

[0070] This invention is a system that solves users' digital challenges in a rapid and personalized manner. This system utilizes the user's operating device, a server, and a generative AI model.

[0071] Users use their own devices to input questions about digital usage. For example, they might ask, "How do I install a new app?" The devices can be operated using a standard keyboard or voice input.

[0072] Questions sent from the terminal are received by the server. The server analyzes the questions and understands their meaning using a natural language processing engine. Natural language processing includes sentence structure analysis and keyword extraction. The extracted information is sent to a generative AI model.

[0073] The generative AI model generates personalized responses based on information received from the server, referencing a knowledge base. The knowledge base is a dynamic information infrastructure that is updated to reflect past conversation history and user characteristics.

[0074] The generated response is optimized by the server and sent to the user's device. This allows the user to quickly resolve digital issues based on the provided information.

[0075] Furthermore, this system can share information with external devices as needed and collaborate in real time. For example, it can send notifications to the devices of family members or support providers to offer additional assistance.

[0076] A concrete example of a prompt would be, "The user wants to know how to install a new app. Please explain the steps in detail." When this prompt is passed to the AI ​​generation model, it can provide a more appropriate response.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user uses a control device to input questions about digital usage. For example, they might input a question like, "I want to know how to install a new app," and send it from the device to the server. The input data is text information about the user's specific problem. The output is the transfer of the user's question to the server.

[0080] Step 2:

[0081] The server receives text information sent from the user's terminal. This information is then passed to the natural language processing engine. The input is raw text data from the user, and the output is structured information for analysis. The server uses the natural language processing engine to analyze the sentence structure and extract keywords. Specifically, this involves morphological analysis of the text and identifying important keywords.

[0082] Step 3:

[0083] The server uses information parsed by the natural language processing engine to formulate prompts for the generative AI model. For example, it might generate a prompt such as, "The user wants to know how to install a new app. Please provide specific instructions." The input is structured parsed information, and the output is a query to the generative AI model.

[0084] Step 4:

[0085] The generative AI model generates the optimal answer by referencing a knowledge base based on prompts received from the server. The input is the prompt text and related knowledge base information, and the output is a specific and personalized answer. It prepares specific answers such as "The download procedure for the XX app is as follows." The generative AI model further refines the answer by taking user characteristics information and past conversation history into consideration.

[0086] Step 5:

[0087] The server reviews the generated responses and prepares them for transmission to the user. The input is the response from the generative AI model, and the output is data formatted for final transmission to the user's terminal. The server removes unnecessary information and adjusts the writing style to an appropriate form.

[0088] Step 6:

[0089] The user receives the final response sent from the server via their control device. Based on this, the user performs the necessary actions on their digital device. The input is the customized response sent from the server, and the output is the user's specific actions and problem-solving. The user uses the information provided by the server to accurately execute the steps to install the application.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern digital purchasing platforms, users face challenges in quickly obtaining detailed product information and receiving personalized online purchasing assistance. This can lead to users spending a significant amount of time trying to find the information they need, potentially resulting in lower satisfaction.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for receiving and analyzing data input from a user information processing device, means for generating a response using a computational AI model based on the analyzed data, and means for personalizing the generated response and transmitting it to the user information processing device. This enables the rapid provision of product information and procedures requested by users at a virtual shopping location.

[0095] A "user information processing device" is a hardware or software device used by users to input data, and includes smartphones and smart glasses.

[0096] "Means for receiving and analyzing data" refers to technical elements that receive data transmitted from a user information processing device and perform procedures to understand its contents.

[0097] A "computational AI model" is an artificial intelligence system used to generate appropriate responses based on input data from a user.

[0098] "Means for personalizing and sending responses" refers to the process of adjusting the generated response based on the characteristics and requests of individual users and sending it to a user information processing device.

[0099] "Interaction history" refers to a record of past interactions between users and the system, and is used as data to improve the user experience in the future.

[0100] A "knowledge base" is a database that a system uses as a reference when providing information, and it is constantly updated to contain the latest information.

[0101] A "virtual shopping location" refers to a digital platform where users can search for and purchase products online, providing them with a virtual shopping experience.

[0102] The system that implements this application example uses a user information processing device, a server, and a computational AI model. Users visit a virtual shopping location using a user information processing device such as a smartphone or smart glasses and input questions and requests. The data entered by the user is sent from the device to the server.

[0103] Upon receiving input data, the server analyzes the sentence structure and intent using language processing techniques. This analysis utilizes natural language processing engines such as SpaCy and NLTK. Based on the analysis results, the server invokes a computational AI model to generate a response optimized for the user's questions and requests. This AI model may include OpenAI® GPT or Google® BERT.

[0104] The generated responses are personalized on the server and sent to the user information processing unit. This process also considers past interaction history and user characteristics, and updates the knowledge base. As a result, users can receive personalized assistance in real time, including detailed product information and purchasing procedures, within the virtual shopping environment.

[0105] As a concrete example, consider a scenario where a user is searching for a new coat on a fashion sales platform using smart glasses. The user asks, "Does this coat come in other colors?" The AI ​​instantly provides multiple color options, simplifying the purchase process.

[0106] Examples of prompt messages include, "Do you have this coat in a different color?"

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] Users input questions and requests regarding the products they wish to purchase via text or voice through their smartphone or smart glasses. The input data is sent to the server as prompt messages. In this process, the user's questions are used as input, and prompt messages are sent to the server as output.

[0110] Step 2:

[0111] The server passes the received prompt sentence to a natural language processing engine (e.g., SpaCy, NLTK) to analyze its structure and intent. This process uses the prompt sentence as input and generates semantic information for words and phrases as output. Specifically, it performs tasks such as keyword extraction and contextual interpretation.

[0112] Step 3:

[0113] The server calls a computational AI model (e.g., OpenAI GPT, Google BERT) based on the analysis results to generate an optimized response to the user's question. In this process, the analysis results are used as input, and the generated response is obtained as output. The AI ​​uses the knowledge it has learned from the training data to provide meaningful information to the user.

[0114] Step 4:

[0115] The server considers the user's past interaction history and profile information, referencing a knowledge base to personalize the generated response. This process uses historical data as input for the purpose of personalizing the response. The output is a custom response tailored to the user's request.

[0116] Step 5:

[0117] The server sends a tailored response to the user's device and provides it to the user through display or audio output. This process uses a custom response as input and displays the response on the user's device as output. Specifically, this might involve displaying the response on the screen or having a voice assistant read the response aloud.

[0118] Step 6:

[0119] If necessary, the server notifies relevant devices of the relevant information. This process allows for the sharing of information with external parties, with the user's permission. Response data is used as input, and notifications are sent to external devices as output.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention relates to a system that, when a user engages in emotionally charged interactions, uses an emotion engine to recognize the user's emotions in real time, and then utilizes a generative AI model to generate and adjust responses based on those emotions. This system mainly consists of a user terminal, a server, an emotion engine, and a generative AI model.

[0122] Users use their devices to ask questions about digital support. In these cases, the user's text input may include emotional elements. For example, they might type, "I'm having a lot of trouble installing the new app."

[0123] The device sends this message to the server. The server analyzes the received message, using a natural language processing engine to understand the sentence structure and intent, and simultaneously using an emotion engine to recognize the user's emotions. Here, for example, the emotion "anxiety" might be detected.

[0124] The server provides recognized emotion information to a generating AI model, which then generates a more appropriate response. The response is adjusted to a tone that takes the recognized emotion into consideration. For example, a gentle response such as, "Don't worry. Let's work together to resolve the app installation issue," might be generated.

[0125] The generated responses are further personalized and sent to the user's device in an appropriate format. The user receives these responses on their device and can solve the problem by following the specific instructions. Additionally, the results of the emotion engine are recorded in a knowledge base, and changes in the user's emotions are tracked.

[0126] This system allows users to seamlessly and safely utilize digital technologies while also receiving emotional support.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user launches the chat app on their device and enters their question or the type of support they need. For example, they might type, "I'm having trouble downloading the app. What should I do?" Sending the message initiates communication with the server.

[0130] Step 2:

[0131] The terminal packets the user's input message, encrypts it, and then sends it to the server. This ensures that the data arrives at the server in a secure state.

[0132] Step 3:

[0133] The server sends the received message to a natural language processing engine for analysis. The engine extracts important keywords and their context from the text to understand the intent of the user's question.

[0134] Step 4:

[0135] At the same time, the server uses an emotion engine to recognize the user's emotions from the received messages. For example, it can detect emotions such as "anxiety" or "impatience" from the expression "I'm in trouble."

[0136] Step 5:

[0137] The server provides the analyzed intent and recognized emotion information to the generating AI model. The model then generates a response based on this information, adjusting the tone and content of the response based on the emotion.

[0138] Step 6:

[0139] The generated responses are further personalized based on the user's past conversation history and profile, and then sent from the server to the user's terminal in a finalized form.

[0140] Step 7:

[0141] The user reviews the response received on their device and takes action based on it. Specifically, they are given concrete instructions such as, "Access the app store, search for the app again, and try downloading it."

[0142] Step 8:

[0143] The server records the results of this interaction and updates the knowledge base with the content of the conversation and sentiment history. This will enable more accurate support for future use.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Conventional information processing systems struggled to generate responses that took user emotions into account, resulting in an inadequate user experience. Furthermore, they lacked mechanisms for effectively generating personalized responses that reflected emotions and for updating information during that process. This limited the services available to users, making it difficult to address individual needs.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for receiving and analyzing data input from a user device, means for recognizing emotions using an information processing engine based on the analyzed data, and means for generating responses using a generative AI algorithm based on the emotions. This enables the automatic generation of personalized responses that reflect the user's emotions and the dynamic updating of knowledge accumulation.

[0149] A "user device" is an electronic device used by a user to input and receive information.

[0150] "Data" refers to the information and messages entered by users, which are the subject of analysis.

[0151] "Means of analysis" refers to a system that has the function of analyzing received data and understanding its content and structure.

[0152] An "information processing engine" is a program that analyzes the content of data and processes it using specific algorithms and methods.

[0153] "Means of recognizing emotions" refers to methods of extracting and understanding emotional elements from user input data.

[0154] A "generative AI algorithm" is an artificial intelligence technology that generates responses based on specified conditions.

[0155] "Means for distributing responses" refers to a system that has the function of sending and displaying the generated responses to the user's device.

[0156] "Knowledge accumulation" is a technology that stores past information and history and updates it as needed.

[0157] The embodiments for carrying out this invention are shown below.

[0158] Users use a device to input specific information, for example, when they need to inquire about or receive support regarding a digital service. This device, such as a computer or smartphone, has the functionality to send the data entered by the user to a server.

[0159] Upon receiving this input data, the server first analyzes the information using natural language processing. Specifically, natural language processing engines such as SpaCy and NLTK are used. Through this engine, the server analyzes the grammatical structure and intent of the data. At the same time, it uses an emotion engine to recognize the user's emotions. For example, IBM Watson® Tone Analyzer or similar emotion recognition tools may be used.

[0160] Based on this analysis, the server uses a generative AI model to generate an appropriate response. Here, GPT-3® or a similar generative AI algorithm is used. This AI model is capable of constructing gentle, specific responses that take user emotions into account in response to user inquiries. An example of a prompt might be, "If the user is confused, create a reassuring suggestion."

[0161] The generated response is further individualized and sent from the server to the user's terminal. The user receives this response on their terminal and can then take action to resolve the specific problem.

[0162] Furthermore, the server records the dialogue history and sentiment analysis results in a knowledge base, which will be used to improve future interactions. This system allows users to receive seamless support that is sensitive to their emotions.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users provide input regarding digital support through their device. This input data is in text format and includes inquiries and requests, such as "I'm having trouble installing a new app." The device receives this input and prepares to send it to the server.

[0166] Step 2:

[0167] The server receives text data from the terminal to be analyzed. First, it uses a natural language processing engine to analyze the grammatical structure and intent of the data. Tools used here include SpaCy and NLTK. Through this analysis, the server clarifies the basic content of the user's inquiry. The output of this step is the grammatically structured data and the interpretation of its intent.

[0168] Step 3:

[0169] The server passes the analyzed information to the emotion engine. The emotion engine uses tools such as IBM Watson Tone Analyzer to extract emotional elements from the text. Here, emotions such as "anxiety" and "confusion" are identified. This emotional information is the main output of this step.

[0170] Step 4:

[0171] The server uses the identified emotion information as a prompt based on a generative AI model. Specifically, the generative AI model is given a prompt such as "How to reassure the user if they are feeling anxious?" The generative AI model (e.g., GPT-3) receives this prompt and generates an emotion-sensitive response. The output is a polite response message that takes the user's emotions into consideration.

[0172] Step 5:

[0173] The generated response is further adjusted by the server to suit the individual user's situation. Past conversation history and user profile are referenced to optimize the answer. The output at this stage is the personalized final response.

[0174] Step 6:

[0175] The server sends the finalized response to the user's terminal. The terminal displays this response to the user. This response provides the user with instructions on what specific action to take. The output of this step is the response received by the user.

[0176] Step 7:

[0177] The server records sentiment information and dialogue results obtained during the response generation process in a knowledge base. This enables more personalized responses in future interactions. The output of this step is the updated knowledge base information.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] To improve the customer service experience in physical stores, there is a need for systems that can grasp customers' emotions in real time and provide appropriate responses according to the situation. However, conventional technologies have not adequately integrated emotion recognition and response generation in customer interactions, resulting in inconsistent service quality and a lack of improvement in customer satisfaction.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for receiving and analyzing information input from a user terminal, means for generating a response using a generation AI model based on the analyzed information, means for individualizing the generated response and transmitting it to the user terminal, means for updating a knowledge database considering past dialogue history and user attributes, means for providing emotional information in real time via an external device, and means for adjusting the content and tone of the response based on the emotional information. This enables staff in physical stores to recognize customers' emotions in real time and provide appropriate and individualized responses accordingly.

[0183] A "user terminal" refers to a device used by a user to input or output information, and includes smart glasses and personal digital assistants (PDAs).

[0184] "Means of analysis" refers to methods and devices for analyzing information received from a user terminal and understanding its structure and intent.

[0185] A "generative AI model" is an artificial intelligence technology model that generates natural language output based on input data.

[0186] "Means of personalized transmission" refers to methods or devices that customize generated responses according to the user's attributes and circumstances and transmit them to the user's terminal.

[0187] A "knowledge database" refers to a collection of information that a system uses to store and learn from past interactions and user data.

[0188] "Means of providing emotional information in real time via external devices" refers to methods or devices that instantly inform users of their emotional state through external devices such as smart glasses.

[0189] "Means of adjusting the content and tone of responses based on emotional information" refers to methods and devices for optimizing the wording and expression of responses according to recognized emotions.

[0190] The system that realizes this invention uses a user terminal, a server, and an external device for recognizing emotions. Smart glasses are used as the user terminal, providing an environment in which the user can easily input information and receive responses.

[0191] First, when a user provides voice input through smart glasses, that information is sent to a server. The server analyzes this voice data and converts it into text data using a speech recognition system. Next, a natural language processing engine is used to analyze the structure and intent of the text. Furthermore, an emotion engine is utilized to recognize the user's emotions in real time and understand the customer's emotional state.

[0192] The recognized emotion information and text data are input into a generative AI model, which generates an appropriate response. The generative AI model adjusts the tone of the response, taking into account the input emotion, enabling more personalized communication. For example, if a user says, "I really like using this product," the server will generate a positive response such as, "Thank you, I'm glad you're satisfied."

[0193] The generated responses are displayed in a personalized format on the user's smart glasses. Furthermore, the dialogue history and user attribute information are stored in a knowledge database and used for future system improvements.

[0194] An example of a prompt message would be: "Based on the customer's statement, 'This bouquet of roses is very beautiful,' generate a positive response that takes into account their joy and interest." This system enables staff to provide high-quality customer service based on customer emotions.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user provides voice input through smart glasses. The voice data is captured by the device and transferred to the server. The voice data becomes input data for post-processing.

[0198] Step 2:

[0199] The server uses a speech recognition system to convert received audio data into text data. It analyzes the audio signal as input and outputs text as character information. This data is used for natural language processing.

[0200] Step 3:

[0201] The server uses a natural language processing engine to analyze the sentence structure and user intent of the text data. It extracts grammatical structure and keywords from the input text to understand the user's intent. This result is then used for sentiment analysis.

[0202] Step 4:

[0203] The server recognizes the user's emotions in real time from text through its emotion engine. Using the parsed text as input, it outputs the emotional state. This information is useful for generating responses.

[0204] Step 5:

[0205] The server inputs emotional information and text data into a generative AI model and generates an appropriate response. It creates prompt sentences that take emotions into account, and the model outputs a more natural response. This response is then delivered to the user.

[0206] Step 6:

[0207] The generated response is personalized on the server and sent to the user's smart glasses. It is displayed as a customized message, enhancing the user's interaction experience.

[0208] Step 7:

[0209] Dialogue history and user attribute information are stored in a knowledge database by the server. This generates data that can be used to improve the system in the future, enabling more accurate services.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention uses a user terminal, a server, and a generative AI model to solve digital challenges faced by users in a rapid and personalized manner. The following describes an embodiment of the system in natural language.

[0227] First, the user uses their device to type a question about their daily digital usage into the chat app. This question might be something like, "I want to know how to download a new app." When the user presses the send button, the message is sent to the server.

[0228] The server passes the received message to a natural language processing engine for analysis. This engine extracts keywords and performs contextual analysis to understand the intent of the user's question. Based on the analysis results, the server calls a generative AI model and starts the process of generating an appropriate answer.

[0229] The generative AI model generates answers to user questions based on a knowledge base. It also considers past conversation history and user profiles to prepare responses optimized for the user. For example, it might provide specific explanations such as, "The download procedure for the XX app is as follows."

[0230] The generated responses are further refined on the server and finally sent to the user's device. The user can then use this information to operate their digital devices. The server can also notify relevant external devices of this interaction and share information with family members or support providers as needed.

[0231] Thus, the system aims to improve the quality of users' digital lives by smoothly resolving their digital problems and providing information safely and reliably.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user activates their device and uses a chat app to enter a question about digital technology. For example, they might type, "How do I download a new app on my smartphone?" Once they have finished typing, they press the send button.

[0235] Step 2:

[0236] The terminal initiates communication to send the message entered by the user to the server. The data is transmitted to the server via the internet in an encrypted state.

[0237] Step 3:

[0238] The server passes the received message to a natural language processing engine. The engine analyzes the message and identifies key keywords and intent. For example, "new app" and "download" might be extracted as key keywords.

[0239] Step 4:

[0240] The server calls an AI model based on the analysis results to generate an appropriate response. The AI ​​model refers to a knowledge base and selects the information that best matches the user's intent to generate the response.

[0241] Step 5:

[0242] The server personalizes the generated responses. This process utilizes past conversation history and user profiles to adjust the responses to be more user-friendly and personalized.

[0243] Step 6:

[0244] The server sends the finalized response to the user's terminal. At this time, if there are settings to notify relevant external devices or family members of the response content, the server will share the information accordingly.

[0245] Step 7:

[0246] The user reviews the response received on their device and operates their smartphone or other device based on the information provided. For example, they can proceed with the operation by receiving specific instructions such as, "Go to the app store, search for XX, and tap download."

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] In modern society, users face a variety of digital challenges, requiring rapid and personalized responses. However, traditional systems only provide single, generic answers, lacking personalized support that takes into account user characteristics and past interactions. This problem degrades the quality of users' digital lives and increases the time it takes to obtain appropriate information.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for receiving and analyzing information input from the user's operating device, means for individualizing the generated response and transmitting it to the user's operating device, means for updating the knowledge base considering past dialogue history and user characteristic information, and means for notifying relevant information in order to share information with external devices. This enables the provision of individualized responses and efficient information sharing.

[0252] A "user control device" is a digital device used by a user to input information and receive output from a system.

[0253] "Information reception and analysis" refers to the process by which a server receives data sent by a user and understands its meaning and intent using natural language processing.

[0254] A "generative AI model" is an artificial intelligence model that generates appropriate responses based on input information while referencing a knowledge base.

[0255] "Personalized responses" refer to information provided to each user that is customized by a generative AI model.

[0256] A "knowledge base" is a collection of information that a system uses to generate answers to user questions, and includes past dialogue history and user characteristic information.

[0257] An "external device" is a device that operates in conjunction with the user's operating device or server and is used to provide additional functions or share information.

[0258] "Related information notification" is a function that sends generated responses or information processed within the system to external devices to facilitate necessary collaboration and information sharing.

[0259] This invention is a system that solves users' digital challenges in a rapid and personalized manner. This system utilizes the user's operating device, a server, and a generative AI model.

[0260] Users use their own devices to input questions about digital usage. For example, they might ask, "How do I install a new app?" The devices can be operated using a standard keyboard or voice input.

[0261] Questions sent from the terminal are received by the server. The server analyzes the questions and understands their meaning using a natural language processing engine. Natural language processing includes sentence structure analysis and keyword extraction. The extracted information is sent to a generative AI model.

[0262] The generative AI model generates personalized responses based on information received from the server, referencing a knowledge base. The knowledge base is a dynamic information infrastructure that is updated to reflect past conversation history and user characteristics.

[0263] The generated response is optimized by the server and sent to the user's device. This allows the user to quickly resolve digital issues based on the provided information.

[0264] Furthermore, this system can share information with external devices as needed and collaborate in real time. For example, it can send notifications to the devices of family members or support providers to offer additional assistance.

[0265] A concrete example of a prompt would be, "The user wants to know how to install a new app. Please explain the steps in detail." When this prompt is passed to the AI ​​generation model, it can provide a more appropriate response.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The user uses a control device to input questions about digital usage. For example, they might input a question like, "I want to know how to install a new app," and send it from the device to the server. The input data is text information about the user's specific problem. The output is the transfer of the user's question to the server.

[0269] Step 2:

[0270] The server receives text information sent from the user's terminal. This information is then passed to the natural language processing engine. The input is raw text data from the user, and the output is structured information for analysis. The server uses the natural language processing engine to analyze the sentence structure and extract keywords. Specifically, this involves morphological analysis of the text and identifying important keywords.

[0271] Step 3:

[0272] The server uses information parsed by the natural language processing engine to formulate prompts for the generative AI model. For example, it might generate a prompt such as, "The user wants to know how to install a new app. Please provide specific instructions." The input is structured parsed information, and the output is a query to the generative AI model.

[0273] Step 4:

[0274] The generative AI model generates the optimal answer by referencing a knowledge base based on prompts received from the server. The input is the prompt text and related knowledge base information, and the output is a specific and personalized answer. It prepares specific answers such as "The download procedure for the XX app is as follows." The generative AI model further refines the answer by taking user characteristics information and past conversation history into consideration.

[0275] Step 5:

[0276] The server reviews the generated responses and prepares them for transmission to the user. The input is the response from the generative AI model, and the output is data formatted for final transmission to the user's terminal. The server removes unnecessary information and adjusts the writing style to an appropriate form.

[0277] Step 6:

[0278] The user receives the final response sent from the server via their control device. Based on this, the user performs the necessary actions on their digital device. The input is the customized response sent from the server, and the output is the user's specific actions and problem-solving. The user uses the information provided by the server to accurately execute the steps to install the application.

[0279] (Application Example 1)

[0280] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0281] In modern digital purchasing platforms, there is a problem that it is difficult for users to quickly obtain detailed information about products or receive individualized online purchasing support. For this reason, users spend a lot of time until they obtain the information they desire, and as a result, their satisfaction may decrease.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes means for receiving and analyzing data input from a user information processing device, means for generating a response using a computational AI model based on the analyzed data, and means for personalizing the generated response and transmitting it to the user information processing device. Thereby, at the virtual purchasing location, it becomes possible to quickly provide the product information and procedures required by the user.

[0284] The "user information processing device" is a hardware or software device for a user to input data, including smartphones, smart glasses, etc.

[0285] The "means for receiving and analyzing data" is a technical element that receives data transmitted from a user information processing device and performs a procedure for understanding its content.

[0286] The "computational AI model" is an artificial intelligence system used to generate an appropriate response based on input data from a user.

[0287] The "means for personalizing and transmitting a response" refers to a process of adjusting the generated response based on the characteristics and requirements of individual users and transmitting it to the user information processing device.

[0288] The "interaction history" is a record of past interactions between a user and a system, and is used as data for improving the future user experience.

[0289] A "knowledge base" is a database that a system uses as a reference when providing information, and it is constantly updated to contain the latest information.

[0290] A "virtual shopping location" refers to a digital platform where users can search for and purchase products online, providing them with a virtual shopping experience.

[0291] The system that implements this application example uses a user information processing device, a server, and a computational AI model. Users visit a virtual shopping location using a user information processing device such as a smartphone or smart glasses and input questions and requests. The data entered by the user is sent from the device to the server.

[0292] Upon receiving input data, the server uses language processing techniques to analyze the sentence structure and intent. This analysis utilizes natural language processing engines such as SpaCy and NLTK. Based on the analysis results, the server invokes a computational AI model to generate a response optimized for the user's questions and requests. Examples of such AI models include OpenAI GPT and Google BERT.

[0293] The generated responses are personalized on the server and sent to the user information processing unit. This process also considers past interaction history and user characteristics, and updates the knowledge base. As a result, users can receive personalized assistance in real time, including detailed product information and purchasing procedures, within the virtual shopping environment.

[0294] As a concrete example, consider a scenario where a user is searching for a new coat on a fashion sales platform using smart glasses. The user asks, "Does this coat come in other colors?" The AI ​​instantly provides multiple color options, simplifying the purchase process.

[0295] Examples of prompt messages include, "Do you have this coat in a different color?"

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] Users input questions and requests regarding the products they wish to purchase via text or voice through their smartphone or smart glasses. The input data is sent to the server as prompt messages. In this process, the user's questions are used as input, and prompt messages are sent to the server as output.

[0299] Step 2:

[0300] The server passes the received prompt sentence to a natural language processing engine (e.g., SpaCy, NLTK) to analyze its structure and intent. This process uses the prompt sentence as input and generates semantic information for words and phrases as output. Specifically, it performs tasks such as keyword extraction and contextual interpretation.

[0301] Step 3:

[0302] The server calls a computational AI model (e.g., OpenAI GPT, Google BERT) based on the analysis results to generate an optimized response to the user's question. In this process, the analysis results are used as input, and the generated response is obtained as output. The AI ​​uses the knowledge it has learned from the training data to provide meaningful information to the user.

[0303] Step 4:

[0304] The server considers the user's past communication history and profile information and refers to the knowledge base for adjustment in order to individualize the generated response. In this process, past data is input for the purpose of personalizing the response. As output, a custom response tailored to the user's request is generated.

[0305] Step 5:

[0306] The server sends the adjusted response to the user's device and provides it to the user through display or voice output. In this process, the custom response is used as input, and the response is displayed on the user's device as output. As specific operations, the response is displayed on the screen or the voice assistant reads out the response.

[0307] Step 6:

[0308] If necessary, the server notifies the relevant device of the relevant information. In this process, information can be shared with the outside after obtaining the user's permission. The response data is used as input, and notification to an external device is performed as output.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0310] The present invention is a system that, when a user conducts an interaction involving emotions, recognizes the user's emotions in real time using an emotion engine, and generates and adjusts a response by utilizing a generated AI model based on that. This system is mainly composed of a user terminal, a server, an emotion engine, and a generated AI model.

[0311] Users use their devices to ask questions about digital support. In these cases, the user's text input may include emotional elements. For example, they might type, "I'm having a lot of trouble installing the new app."

[0312] The device sends this message to the server. The server analyzes the received message, using a natural language processing engine to understand the sentence structure and intent, and simultaneously using an emotion engine to recognize the user's emotions. Here, for example, the emotion "anxiety" might be detected.

[0313] The server provides recognized emotion information to a generating AI model, which then generates a more appropriate response. The response is adjusted to a tone that takes the recognized emotion into consideration. For example, a gentle response such as, "Don't worry. Let's work together to resolve the app installation issue," might be generated.

[0314] The generated responses are further personalized and sent to the user's device in an appropriate format. The user receives these responses on their device and can solve the problem by following the specific instructions. Additionally, the results of the emotion engine are recorded in a knowledge base, and changes in the user's emotions are tracked.

[0315] This system allows users to seamlessly and safely utilize digital technologies while also receiving emotional support.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The user launches the chat app on their device and enters their question or the type of support they need. For example, they might type, "I'm having trouble downloading the app. What should I do?" Sending the message initiates communication with the server.

[0319] Step 2:

[0320] The terminal packets the user's input message, encrypts it, and then sends it to the server. This ensures that the data arrives at the server in a secure state.

[0321] Step 3:

[0322] The server sends the received message to a natural language processing engine for analysis. The engine extracts important keywords and their context from the text to understand the intent of the user's question.

[0323] Step 4:

[0324] At the same time, the server uses an emotion engine to recognize the user's emotions from the received messages. For example, it can detect emotions such as "anxiety" or "impatience" from the expression "I'm in trouble."

[0325] Step 5:

[0326] The server provides the analyzed intent and recognized emotion information to the generating AI model. The model then generates a response based on this information, adjusting the tone and content of the response based on the emotion.

[0327] Step 6:

[0328] The generated responses are further personalized based on the user's past conversation history and profile, and then sent from the server to the user's terminal in a finalized form.

[0329] Step 7:

[0330] The user reviews the response received on their device and takes action based on it. Specifically, they are given concrete instructions such as, "Access the app store, search for the app again, and try downloading it."

[0331] Step 8:

[0332] The server records the results of this interaction and updates the knowledge base with the content of the conversation and sentiment history. This will enable more accurate support for future use.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] Conventional information processing systems struggled to generate responses that took user emotions into account, resulting in an inadequate user experience. Furthermore, they lacked mechanisms for effectively generating personalized responses that reflected emotions and for updating information during that process. This limited the services available to users, making it difficult to address individual needs.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for receiving and analyzing data input from a user device, means for recognizing emotions using an information processing engine based on the analyzed data, and means for generating responses using a generative AI algorithm based on the emotions. This enables the automatic generation of personalized responses that reflect the user's emotions and the dynamic updating of knowledge accumulation.

[0338] A "user device" is an electronic device used by a user to input and receive information.

[0339] "Data" refers to the information and messages entered by users, which are the subject of analysis.

[0340] "Means of analysis" refers to a system that has the function of analyzing received data and understanding its content and structure.

[0341] An "information processing engine" is a program that analyzes the content of data and processes it using specific algorithms and methods.

[0342] "Means of recognizing emotions" refers to methods of extracting and understanding emotional elements from user input data.

[0343] A "generative AI algorithm" is an artificial intelligence technology that generates responses based on specified conditions.

[0344] "Means for distributing responses" refers to a system that has the function of sending and displaying the generated responses to the user's device.

[0345] "Knowledge accumulation" is a technology that stores past information and history and updates it as needed.

[0346] The embodiments for carrying out this invention are shown below.

[0347] Users use a device to input specific information, for example, when they need to inquire about or receive support regarding a digital service. This device, such as a computer or smartphone, has the functionality to send the data entered by the user to a server.

[0348] Upon receiving this input data, the server first analyzes the information using natural language processing. Specifically, natural language processing engines such as SpaCy and NLTK are used. Through this engine, the server analyzes the grammatical structure and intent of the data. At the same time, it uses an emotion engine to recognize the user's emotions. For example, IBM Watson Tone Analyzer or similar emotion recognition tools may be used.

[0349] Based on this analysis, the server uses a generative AI model to generate an appropriate response. Here, GPT-3 or a similar generative AI algorithm is used. This AI model is capable of constructing gentle, specific responses that take user emotions into account in response to user inquiries. An example of a prompt might be, "If the user is confused, create a reassuring suggestion."

[0350] The generated response is further individualized and sent from the server to the user's terminal. The user receives this response on their terminal and can then take action to resolve the specific problem.

[0351] Furthermore, the server records the dialogue history and sentiment analysis results in a knowledge base, which will be used to improve future interactions. This system allows users to receive seamless support that is sensitive to their emotions.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] Users provide input regarding digital support through their device. This input data is in text format and includes inquiries and requests, such as "I'm having trouble installing a new app." The device receives this input and prepares to send it to the server.

[0355] Step 2:

[0356] The server receives text data from the terminal to be analyzed. First, it uses a natural language processing engine to analyze the grammatical structure and intent of the data. Tools used here include SpaCy and NLTK. Through this analysis, the server clarifies the basic content of the user's inquiry. The output of this step is the grammatically structured data and the interpretation of its intent.

[0357] Step 3:

[0358] The server passes the analyzed information to the emotion engine. The emotion engine uses tools such as IBM Watson Tone Analyzer to extract emotional elements from the text. Here, emotions such as "anxiety" and "confusion" are identified. This emotional information is the main output of this step.

[0359] Step 4:

[0360] The server uses the identified emotion information as a prompt based on a generative AI model. Specifically, the generative AI model is given a prompt such as "How to reassure the user if they are feeling anxious?" The generative AI model (e.g., GPT-3) receives this prompt and generates an emotion-sensitive response. The output is a polite response message that takes the user's emotions into consideration.

[0361] Step 5:

[0362] The generated response is further adjusted by the server to suit the individual user's situation. Past conversation history and user profile are referenced to optimize the answer. The output at this stage is the personalized final response.

[0363] Step 6:

[0364] The server sends the finalized response to the user's terminal. The terminal displays this response to the user. This response provides the user with instructions on what specific action to take. The output of this step is the response received by the user.

[0365] Step 7:

[0366] The server records sentiment information and dialogue results obtained during the response generation process in a knowledge base. This enables more personalized responses in future interactions. The output of this step is the updated knowledge base information.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0369] To improve the customer service experience in physical stores, there is a need for systems that can grasp customers' emotions in real time and provide appropriate responses according to the situation. However, conventional technologies have not adequately integrated emotion recognition and response generation in customer interactions, resulting in inconsistent service quality and a lack of improvement in customer satisfaction.

[0370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0371] In this invention, the server includes means for receiving and analyzing information input from a user terminal, means for generating a response using a generation AI model based on the analyzed information, means for individualizing the generated response and transmitting it to the user terminal, means for updating a knowledge database considering past dialogue history and user attributes, means for providing emotional information in real time via an external device, and means for adjusting the content and tone of the response based on the emotional information. This enables staff in physical stores to recognize customers' emotions in real time and provide appropriate and individualized responses accordingly.

[0372] A "user terminal" refers to a device used by a user to input or output information, and includes smart glasses and personal digital assistants (PDAs).

[0373] "Means of analysis" refers to methods and devices for analyzing information received from a user terminal and understanding its structure and intent.

[0374] A "generative AI model" is an artificial intelligence technology model that generates natural language output based on input data.

[0375] "Means of personalized transmission" refers to methods or devices that customize generated responses according to the user's attributes and circumstances and transmit them to the user's terminal.

[0376] A "knowledge database" refers to a collection of information that a system uses to store and learn from past interactions and user data.

[0377] "Means of providing emotional information in real time via external devices" refers to methods or devices that instantly inform users of their emotional state through external devices such as smart glasses.

[0378] "Means of adjusting the content and tone of responses based on emotional information" refers to methods and devices for optimizing the wording and expression of responses according to recognized emotions.

[0379] The system that realizes this invention uses a user terminal, a server, and an external device for recognizing emotions. Smart glasses are used as the user terminal, providing an environment in which the user can easily input information and receive responses.

[0380] First, when a user provides voice input through smart glasses, that information is sent to a server. The server analyzes this voice data and converts it into text data using a speech recognition system. Next, a natural language processing engine is used to analyze the structure and intent of the text. Furthermore, an emotion engine is utilized to recognize the user's emotions in real time and understand the customer's emotional state.

[0381] The recognized emotion information and text data are input into a generative AI model, which generates an appropriate response. The generative AI model adjusts the tone of the response, taking into account the input emotion, enabling more personalized communication. For example, if a user says, "I really like using this product," the server will generate a positive response such as, "Thank you, I'm glad you're satisfied."

[0382] The generated responses are displayed in a personalized format on the user's smart glasses. Furthermore, the dialogue history and user attribute information are stored in a knowledge database and used for future system improvements.

[0383] An example of a prompt message would be: "Based on the customer's statement, 'This bouquet of roses is very beautiful,' generate a positive response that takes into account their joy and interest." This system enables staff to provide high-quality customer service based on customer emotions.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The user provides voice input through smart glasses. The voice data is captured by the device and transferred to the server. The voice data becomes input data for post-processing.

[0387] Step 2:

[0388] The server uses a speech recognition system to convert received audio data into text data. It analyzes the audio signal as input and outputs text as character information. This data is used for natural language processing.

[0389] Step 3:

[0390] The server uses a natural language processing engine to analyze the sentence structure and user intent of the text data. It extracts grammatical structure and keywords from the input text to understand the user's intent. This result is then used for sentiment analysis.

[0391] Step 4:

[0392] The server recognizes the user's emotions in real time from text through its emotion engine. Using the parsed text as input, it outputs the emotional state. This information is useful for generating responses.

[0393] Step 5:

[0394] The server inputs emotional information and text data into a generative AI model and generates an appropriate response. It creates prompt sentences that take emotions into account, and the model outputs a more natural response. This response is then delivered to the user.

[0395] Step 6:

[0396] The generated response is personalized on the server and sent to the user's smart glasses. It is displayed as a customized message, enhancing the user's interaction experience.

[0397] Step 7:

[0398] Dialogue history and user attribute information are stored in a knowledge database by the server. This generates data that can be used to improve the system in the future, enabling more accurate services.

[0399] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0400] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0405] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0407] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0409] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0410] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0411] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0412] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0414] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0415] This invention uses a user terminal, a server, and a generative AI model to solve digital challenges faced by users in a rapid and personalized manner. The following describes an embodiment of the system in natural language.

[0416] First, the user uses their device to type a question about their daily digital usage into the chat app. This question might be something like, "I want to know how to download a new app." When the user presses the send button, the message is sent to the server.

[0417] The server passes the received message to a natural language processing engine for analysis. This engine extracts keywords and performs contextual analysis to understand the intent of the user's question. Based on the analysis results, the server calls a generative AI model and starts the process of generating an appropriate answer.

[0418] The generative AI model generates answers to user questions based on a knowledge base. It also considers past conversation history and user profiles to prepare responses optimized for the user. For example, it might provide specific explanations such as, "The download procedure for the XX app is as follows."

[0419] The generated responses are further refined on the server and finally sent to the user's device. The user can then use this information to operate their digital devices. The server can also notify relevant external devices of this interaction and share information with family members or support providers as needed.

[0420] Thus, the system aims to improve the quality of users' digital lives by smoothly resolving their digital problems and providing information safely and reliably.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The user activates their device and uses a chat app to enter a question about digital technology. For example, they might type, "How do I download a new app on my smartphone?" Once they have finished typing, they press the send button.

[0424] Step 2:

[0425] The terminal initiates communication to send the message entered by the user to the server. The data is transmitted to the server via the internet in an encrypted state.

[0426] Step 3:

[0427] The server passes the received message to a natural language processing engine. The engine analyzes the message and identifies key keywords and intent. For example, "new app" and "download" might be extracted as key keywords.

[0428] Step 4:

[0429] The server calls an AI model based on the analysis results to generate an appropriate response. The AI ​​model refers to a knowledge base and selects the information that best matches the user's intent to generate the response.

[0430] Step 5:

[0431] The server personalizes the generated responses. This process utilizes past conversation history and user profiles to adjust the responses to be more user-friendly and personalized.

[0432] Step 6:

[0433] The server sends the finalized response to the user's terminal. At this time, if there are settings to notify relevant external devices or family members of the response content, the server will share the information accordingly.

[0434] Step 7:

[0435] The user reviews the response received on their device and operates their smartphone or other device based on the information provided. For example, they can proceed with the operation by receiving specific instructions such as, "Go to the app store, search for XX, and tap download."

[0436] (Example 1)

[0437] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0438] In modern society, users face a variety of digital challenges, requiring rapid and personalized responses. However, traditional systems only provide single, generic answers, lacking personalized support that takes into account user characteristics and past interactions. This problem degrades the quality of users' digital lives and increases the time it takes to obtain appropriate information.

[0439] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0440] In this invention, the server includes means for receiving and analyzing information input from the user's operating device, means for individualizing the generated response and transmitting it to the user's operating device, means for updating the knowledge base considering past dialogue history and user characteristic information, and means for notifying relevant information in order to share information with external devices. This enables the provision of individualized responses and efficient information sharing.

[0441] A "user control device" is a digital device used by a user to input information and receive output from a system.

[0442] "Information reception and analysis" refers to the process by which a server receives data sent by a user and understands its meaning and intent using natural language processing.

[0443] A "generative AI model" is an artificial intelligence model that generates appropriate responses based on input information while referencing a knowledge base.

[0444] "Personalized responses" refer to information provided to each user that is customized by a generative AI model.

[0445] A "knowledge base" is a collection of information that a system uses to generate answers to user questions, and includes past dialogue history and user characteristic information.

[0446] An "external device" is a device that operates in conjunction with the user's operating device or server and is used to provide additional functions or share information.

[0447] "Related information notification" is a function that sends generated responses or information processed within the system to external devices to facilitate necessary collaboration and information sharing.

[0448] This invention is a system that solves users' digital challenges in a rapid and personalized manner. This system utilizes the user's operating device, a server, and a generative AI model.

[0449] Users use their own devices to input questions about digital usage. For example, they might ask, "How do I install a new app?" The devices can be operated using a standard keyboard or voice input.

[0450] Questions sent from the terminal are received by the server. The server analyzes the questions and understands their meaning using a natural language processing engine. Natural language processing includes sentence structure analysis and keyword extraction. The extracted information is sent to a generative AI model.

[0451] The generative AI model generates personalized responses based on information received from the server, referencing a knowledge base. The knowledge base is a dynamic information infrastructure that is updated to reflect past conversation history and user characteristics.

[0452] The generated response is optimized by the server and sent to the user's device. This allows the user to quickly resolve digital issues based on the provided information.

[0453] Furthermore, this system can share information with external devices as needed and collaborate in real time. For example, it can send notifications to the devices of family members or support providers to offer additional assistance.

[0454] A concrete example of a prompt would be, "The user wants to know how to install a new app. Please explain the steps in detail." When this prompt is passed to the AI ​​generation model, it can provide a more appropriate response.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The user uses a control device to input questions about digital usage. For example, they might input a question like, "I want to know how to install a new app," and send it from the device to the server. The input data is text information about the user's specific problem. The output is the transfer of the user's question to the server.

[0458] Step 2:

[0459] The server receives text information sent from the user's terminal. This information is then passed to the natural language processing engine. The input is raw text data from the user, and the output is structured information for analysis. The server uses the natural language processing engine to analyze the sentence structure and extract keywords. Specifically, this involves morphological analysis of the text and identifying important keywords.

[0460] Step 3:

[0461] The server uses information parsed by the natural language processing engine to formulate prompts for the generative AI model. For example, it might generate a prompt such as, "The user wants to know how to install a new app. Please provide specific instructions." The input is structured parsed information, and the output is a query to the generative AI model.

[0462] Step 4:

[0463] The generative AI model generates the optimal answer by referencing a knowledge base based on prompts received from the server. The input is the prompt text and related knowledge base information, and the output is a specific and personalized answer. It prepares specific answers such as "The download procedure for the XX app is as follows." The generative AI model further refines the answer by taking user characteristics information and past conversation history into consideration.

[0464] Step 5:

[0465] The server reviews the generated responses and prepares them for transmission to the user. The input is the response from the generative AI model, and the output is data formatted for final transmission to the user's terminal. The server removes unnecessary information and adjusts the writing style to an appropriate form.

[0466] Step 6:

[0467] The user receives the final response sent from the server via their control device. Based on this, the user performs the necessary actions on their digital device. The input is the customized response sent from the server, and the output is the user's specific actions and problem-solving. The user uses the information provided by the server to accurately execute the steps to install the application.

[0468] (Application Example 1)

[0469] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] In modern digital purchasing platforms, users face challenges in quickly obtaining detailed product information and receiving personalized online purchasing assistance. This can lead to users spending a significant amount of time trying to find the information they need, potentially resulting in lower satisfaction.

[0471] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0472] In this invention, the server includes means for receiving and analyzing data input from a user information processing device, means for generating a response using a computational AI model based on the analyzed data, and means for personalizing the generated response and transmitting it to the user information processing device. This enables the rapid provision of product information and procedures requested by users at a virtual shopping location.

[0473] A "user information processing device" is a hardware or software device used by users to input data, and includes smartphones and smart glasses.

[0474] "Means for receiving and analyzing data" refers to technical elements that receive data transmitted from a user information processing device and perform procedures to understand its contents.

[0475] A "computational AI model" is an artificial intelligence system used to generate appropriate responses based on input data from a user.

[0476] "Means for personalizing and sending responses" refers to the process of adjusting the generated response based on the characteristics and requests of individual users and sending it to a user information processing device.

[0477] "Interaction history" refers to a record of past interactions between users and the system, and is used as data to improve the user experience in the future.

[0478] A "knowledge base" is a database that a system uses as a reference when providing information, and it is constantly updated to contain the latest information.

[0479] A "virtual shopping location" refers to a digital platform where users can search for and purchase products online, providing them with a virtual shopping experience.

[0480] The system that implements this application example uses a user information processing device, a server, and a computational AI model. Users visit a virtual shopping location using a user information processing device such as a smartphone or smart glasses and input questions and requests. The data entered by the user is sent from the device to the server.

[0481] Upon receiving input data, the server uses language processing techniques to analyze the sentence structure and intent. This analysis utilizes natural language processing engines such as SpaCy and NLTK. Based on the analysis results, the server invokes a computational AI model to generate a response optimized for the user's questions and requests. Examples of such AI models include OpenAI GPT and Google BERT.

[0482] The generated responses are personalized on the server and sent to the user information processing unit. This process also considers past interaction history and user characteristics, and updates the knowledge base. As a result, users can receive personalized assistance in real time, including detailed product information and purchasing procedures, within the virtual shopping environment.

[0483] As a concrete example, consider a scenario where a user is searching for a new coat on a fashion sales platform using smart glasses. The user asks, "Does this coat come in other colors?" The AI ​​instantly provides multiple color options, simplifying the purchase process.

[0484] Examples of prompt messages include, "Do you have this coat in a different color?"

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] Users input questions and requests regarding the products they wish to purchase via text or voice through their smartphone or smart glasses. The input data is sent to the server as prompt messages. In this process, the user's questions are used as input, and prompt messages are sent to the server as output.

[0488] Step 2:

[0489] The server passes the received prompt sentence to a natural language processing engine (e.g., SpaCy, NLTK) to analyze its structure and intent. This process uses the prompt sentence as input and generates semantic information for words and phrases as output. Specifically, it performs tasks such as keyword extraction and contextual interpretation.

[0490] Step 3:

[0491] The server calls a computational AI model (e.g., OpenAI GPT, Google BERT) based on the analysis results to generate an optimized response to the user's question. In this process, the analysis results are used as input, and the generated response is obtained as output. The AI ​​uses the knowledge it has learned from the training data to provide meaningful information to the user.

[0492] Step 4:

[0493] The server considers the user's past interaction history and profile information, referencing a knowledge base to personalize the generated response. This process uses historical data as input for the purpose of personalizing the response. The output is a custom response tailored to the user's request.

[0494] Step 5:

[0495] The server sends a tailored response to the user's device and provides it to the user through display or audio output. This process uses a custom response as input and displays the response on the user's device as output. Specifically, this might involve displaying the response on the screen or having a voice assistant read the response aloud.

[0496] Step 6:

[0497] If necessary, the server notifies relevant devices of the relevant information. This process allows for the sharing of information with external parties, with the user's permission. Response data is used as input, and notifications are sent to external devices as output.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention relates to a system that, when a user engages in emotionally charged interactions, uses an emotion engine to recognize the user's emotions in real time, and then utilizes a generative AI model to generate and adjust responses based on those emotions. This system mainly consists of a user terminal, a server, an emotion engine, and a generative AI model.

[0500] Users use their devices to ask questions about digital support. In these cases, the user's text input may include emotional elements. For example, they might type, "I'm having a lot of trouble installing the new app."

[0501] The device sends this message to the server. The server analyzes the received message, using a natural language processing engine to understand the sentence structure and intent, and simultaneously using an emotion engine to recognize the user's emotions. Here, for example, the emotion "anxiety" might be detected.

[0502] The server provides recognized emotion information to a generating AI model, which then generates a more appropriate response. The response is adjusted to a tone that takes the recognized emotion into consideration. For example, a gentle response such as, "Don't worry. Let's work together to resolve the app installation issue," might be generated.

[0503] The generated responses are further personalized and sent to the user's device in an appropriate format. The user receives these responses on their device and can solve the problem by following the specific instructions. Additionally, the results of the emotion engine are recorded in a knowledge base, and changes in the user's emotions are tracked.

[0504] This system allows users to seamlessly and safely utilize digital technologies while also receiving emotional support.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The user launches the chat app on their device and enters their question or the type of support they need. For example, they might type, "I'm having trouble downloading the app. What should I do?" Sending the message initiates communication with the server.

[0508] Step 2:

[0509] The terminal packets the user's input message, encrypts it, and then sends it to the server. This ensures that the data arrives at the server in a secure state.

[0510] Step 3:

[0511] The server sends the received message to a natural language processing engine for analysis. The engine extracts important keywords and their context from the text to understand the intent of the user's question.

[0512] Step 4:

[0513] At the same time, the server uses an emotion engine to recognize the user's emotions from the received messages. For example, it can detect emotions such as "anxiety" or "impatience" from the expression "I'm in trouble."

[0514] Step 5:

[0515] The server provides the analyzed intent and recognized emotion information to the generating AI model. The model then generates a response based on this information, adjusting the tone and content of the response based on the emotion.

[0516] Step 6:

[0517] The generated responses are further personalized based on the user's past conversation history and profile, and then sent from the server to the user's terminal in a finalized form.

[0518] Step 7:

[0519] The user reviews the response received on their device and takes action based on it. Specifically, they are given concrete instructions such as, "Access the app store, search for the app again, and try downloading it."

[0520] Step 8:

[0521] The server records the results of this interaction and updates the knowledge base with the content of the conversation and sentiment history. This will enable more accurate support for future use.

[0522] (Example 2)

[0523] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] Conventional information processing systems struggled to generate responses that took user emotions into account, resulting in an inadequate user experience. Furthermore, they lacked mechanisms for effectively generating personalized responses that reflected emotions and for updating information during that process. This limited the services available to users, making it difficult to address individual needs.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0526] In this invention, the server includes means for receiving and analyzing data input from a user device, means for recognizing emotions using an information processing engine based on the analyzed data, and means for generating responses using a generative AI algorithm based on the emotions. This enables the automatic generation of personalized responses that reflect the user's emotions and the dynamic updating of knowledge accumulation.

[0527] A "user device" is an electronic device used by a user to input and receive information.

[0528] "Data" refers to the information and messages entered by users, which are the subject of analysis.

[0529] "Means of analysis" refers to a system that has the function of analyzing received data and understanding its content and structure.

[0530] An "information processing engine" is a program that analyzes the content of data and processes it using specific algorithms and methods.

[0531] "Means of recognizing emotions" refers to methods of extracting and understanding emotional elements from user input data.

[0532] A "generative AI algorithm" is an artificial intelligence technology that generates responses based on specified conditions.

[0533] "Means for distributing responses" refers to a system that has the function of sending and displaying the generated responses to the user's device.

[0534] "Knowledge accumulation" is a technology that stores past information and history and updates it as needed.

[0535] The embodiments for carrying out this invention are shown below.

[0536] Users use a device to input specific information, for example, when they need to inquire about or receive support regarding a digital service. This device, such as a computer or smartphone, has the functionality to send the data entered by the user to a server.

[0537] Upon receiving this input data, the server first analyzes the information using natural language processing. Specifically, natural language processing engines such as SpaCy and NLTK are used. Through this engine, the server analyzes the grammatical structure and intent of the data. At the same time, it uses an emotion engine to recognize the user's emotions. For example, IBM Watson Tone Analyzer or similar emotion recognition tools may be used.

[0538] Based on this analysis, the server uses a generative AI model to generate an appropriate response. Here, GPT-3 or a similar generative AI algorithm is used. This AI model is capable of constructing gentle, specific responses that take user emotions into account in response to user inquiries. An example of a prompt might be, "If the user is confused, create a reassuring suggestion."

[0539] The generated response is further individualized and sent from the server to the user's terminal. The user receives this response on their terminal and can then take action to resolve the specific problem.

[0540] Furthermore, the server records the dialogue history and sentiment analysis results in a knowledge base, which will be used to improve future interactions. This system allows users to receive seamless support that is sensitive to their emotions.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] Users provide input regarding digital support through their device. This input data is in text format and includes inquiries and requests, such as "I'm having trouble installing a new app." The device receives this input and prepares to send it to the server.

[0544] Step 2:

[0545] The server receives text data from the terminal to be analyzed. First, it uses a natural language processing engine to analyze the grammatical structure and intent of the data. Tools used here include SpaCy and NLTK. Through this analysis, the server clarifies the basic content of the user's inquiry. The output of this step is the grammatically structured data and the interpretation of its intent.

[0546] Step 3:

[0547] The server passes the analyzed information to the emotion engine. The emotion engine uses tools such as IBM Watson Tone Analyzer to extract emotional elements from the text. Here, emotions such as "anxiety" and "confusion" are identified. This emotional information is the main output of this step.

[0548] Step 4:

[0549] The server uses the identified emotion information as a prompt based on a generative AI model. Specifically, the generative AI model is given a prompt such as "How to reassure the user if they are feeling anxious?" The generative AI model (e.g., GPT-3) receives this prompt and generates an emotion-sensitive response. The output is a polite response message that takes the user's emotions into consideration.

[0550] Step 5:

[0551] The generated response is further adjusted by the server to suit the individual user's situation. Past conversation history and user profile are referenced to optimize the answer. The output at this stage is the personalized final response.

[0552] Step 6:

[0553] The server sends the finalized response to the user's terminal. The terminal displays this response to the user. This response provides the user with instructions on what specific action to take. The output of this step is the response received by the user.

[0554] Step 7:

[0555] The server records sentiment information and dialogue results obtained during the response generation process in a knowledge base. This enables more personalized responses in future interactions. The output of this step is the updated knowledge base information.

[0556] (Application Example 2)

[0557] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0558] To improve the customer service experience in physical stores, there is a need for systems that can grasp customers' emotions in real time and provide appropriate responses according to the situation. However, conventional technologies have not adequately integrated emotion recognition and response generation in customer interactions, resulting in inconsistent service quality and a lack of improvement in customer satisfaction.

[0559] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0560] In this invention, the server includes means for receiving and analyzing information input from a user terminal, means for generating a response using a generation AI model based on the analyzed information, means for individualizing the generated response and transmitting it to the user terminal, means for updating a knowledge database considering past dialogue history and user attributes, means for providing emotional information in real time via an external device, and means for adjusting the content and tone of the response based on the emotional information. This enables staff in physical stores to recognize customers' emotions in real time and provide appropriate and individualized responses accordingly.

[0561] A "user terminal" refers to a device used by a user to input or output information, and includes smart glasses and personal digital assistants (PDAs).

[0562] "Means of analysis" refers to methods and devices for analyzing information received from a user terminal and understanding its structure and intent.

[0563] A "generative AI model" is an artificial intelligence technology model that generates natural language output based on input data.

[0564] "Means of personalized transmission" refers to methods or devices that customize generated responses according to the user's attributes and circumstances and transmit them to the user's terminal.

[0565] A "knowledge database" refers to a collection of information that a system uses to store and learn from past interactions and user data.

[0566] "Means of providing emotional information in real time via external devices" refers to methods or devices that instantly inform users of their emotional state through external devices such as smart glasses.

[0567] "Means of adjusting the content and tone of responses based on emotional information" refers to methods and devices for optimizing the wording and expression of responses according to recognized emotions.

[0568] The system that realizes this invention uses a user terminal, a server, and an external device for recognizing emotions. Smart glasses are used as the user terminal, providing an environment in which the user can easily input information and receive responses.

[0569] First, when a user provides voice input through smart glasses, that information is sent to a server. The server analyzes this voice data and converts it into text data using a speech recognition system. Next, a natural language processing engine is used to analyze the structure and intent of the text. Furthermore, an emotion engine is utilized to recognize the user's emotions in real time and understand the customer's emotional state.

[0570] The recognized emotion information and text data are input into a generative AI model, which generates an appropriate response. The generative AI model adjusts the tone of the response, taking into account the input emotion, enabling more personalized communication. For example, if a user says, "I really like using this product," the server will generate a positive response such as, "Thank you, I'm glad you're satisfied."

[0571] The generated responses are displayed in a personalized format on the user's smart glasses. Furthermore, the dialogue history and user attribute information are stored in a knowledge database and used for future system improvements.

[0572] An example of a prompt message would be: "Based on the customer's statement, 'This bouquet of roses is very beautiful,' generate a positive response that takes into account their joy and interest." This system enables staff to provide high-quality customer service based on customer emotions.

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The user provides voice input through smart glasses. The voice data is captured by the device and transferred to the server. The voice data becomes input data for post-processing.

[0576] Step 2:

[0577] The server uses a speech recognition system to convert received audio data into text data. It analyzes the audio signal as input and outputs text as character information. This data is used for natural language processing.

[0578] Step 3:

[0579] The server uses a natural language processing engine to analyze the sentence structure and user intent of the text data. It extracts grammatical structure and keywords from the input text to understand the user's intent. This result is then used for sentiment analysis.

[0580] Step 4:

[0581] The server recognizes the user's emotions in real time from text through its emotion engine. Using the parsed text as input, it outputs the emotional state. This information is useful for generating responses.

[0582] Step 5:

[0583] The server inputs emotional information and text data into a generative AI model and generates an appropriate response. It creates prompt sentences that take emotions into account, and the model outputs a more natural response. This response is then delivered to the user.

[0584] Step 6:

[0585] The generated response is personalized on the server and sent to the user's smart glasses. It is displayed as a customized message, enhancing the user's interaction experience.

[0586] Step 7:

[0587] Dialogue history and user attribute information are stored in a knowledge database by the server. This generates data that can be used to improve the system in the future, enabling more accurate services.

[0588] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0589] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0590] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0594] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0595] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0596] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0597] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0598] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0599] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0600] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0601] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0602] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0603] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0604] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0605] This invention uses a user terminal, a server, and a generative AI model to solve digital challenges faced by users in a rapid and personalized manner. The following describes an embodiment of the system in natural language.

[0606] First, the user uses their device to type a question about their daily digital usage into the chat app. This question might be something like, "I want to know how to download a new app." When the user presses the send button, the message is sent to the server.

[0607] The server passes the received message to a natural language processing engine for analysis. This engine extracts keywords and performs contextual analysis to understand the intent of the user's question. Based on the analysis results, the server calls a generative AI model and starts the process of generating an appropriate answer.

[0608] The generative AI model generates answers to user questions based on a knowledge base. It also considers past conversation history and user profiles to prepare responses optimized for the user. For example, it might provide specific explanations such as, "The download procedure for the XX app is as follows."

[0609] The generated responses are further refined on the server and finally sent to the user's device. The user can then use this information to operate their digital devices. The server can also notify relevant external devices of this interaction and share information with family members or support providers as needed.

[0610] Thus, the system aims to improve the quality of users' digital lives by smoothly resolving their digital problems and providing information safely and reliably.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The user activates their device and uses a chat app to enter a question about digital technology. For example, they might type, "How do I download a new app on my smartphone?" Once they have finished typing, they press the send button.

[0614] Step 2:

[0615] The terminal initiates communication to send the message entered by the user to the server. The data is transmitted to the server via the internet in an encrypted state.

[0616] Step 3:

[0617] The server passes the received message to a natural language processing engine. The engine analyzes the message and identifies key keywords and intent. For example, "new app" and "download" might be extracted as key keywords.

[0618] Step 4:

[0619] The server calls an AI model based on the analysis results to generate an appropriate response. The AI ​​model refers to a knowledge base and selects the information that best matches the user's intent to generate the response.

[0620] Step 5:

[0621] The server personalizes the generated responses. This process utilizes past conversation history and user profiles to adjust the responses to be more user-friendly and personalized.

[0622] Step 6:

[0623] The server sends the finalized response to the user's terminal. At this time, if there are settings to notify relevant external devices or family members of the response content, the server will share the information accordingly.

[0624] Step 7:

[0625] The user reviews the response received on their device and operates their smartphone or other device based on the information provided. For example, they can proceed with the operation by receiving specific instructions such as, "Go to the app store, search for XX, and tap download."

[0626] (Example 1)

[0627] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] In modern society, users face a variety of digital challenges, requiring rapid and personalized responses. However, traditional systems only provide single, generic answers, lacking personalized support that takes into account user characteristics and past interactions. This problem degrades the quality of users' digital lives and increases the time it takes to obtain appropriate information.

[0629] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0630] In this invention, the server includes means for receiving and analyzing information input from the user's operating device, means for individualizing the generated response and transmitting it to the user's operating device, means for updating the knowledge base considering past dialogue history and user characteristic information, and means for notifying relevant information in order to share information with external devices. This enables the provision of individualized responses and efficient information sharing.

[0631] A "user control device" is a digital device used by a user to input information and receive output from a system.

[0632] "Information reception and analysis" refers to the process by which a server receives data sent by a user and understands its meaning and intent using natural language processing.

[0633] A "generative AI model" is an artificial intelligence model that generates appropriate responses based on input information while referencing a knowledge base.

[0634] "Personalized responses" refer to information provided to each user that is customized by a generative AI model.

[0635] A "knowledge base" is a collection of information that a system uses to generate answers to user questions, and includes past dialogue history and user characteristic information.

[0636] An "external device" is a device that operates in conjunction with the user's operating device or server and is used to provide additional functions or share information.

[0637] "Related information notification" is a function that sends generated responses or information processed within the system to external devices to facilitate necessary collaboration and information sharing.

[0638] This invention is a system that solves users' digital challenges in a rapid and personalized manner. This system utilizes the user's operating device, a server, and a generative AI model.

[0639] Users use their own devices to input questions about digital usage. For example, they might ask, "How do I install a new app?" The devices can be operated using a standard keyboard or voice input.

[0640] Questions sent from the terminal are received by the server. The server analyzes the questions and understands their meaning using a natural language processing engine. Natural language processing includes sentence structure analysis and keyword extraction. The extracted information is sent to a generative AI model.

[0641] The generative AI model generates personalized responses based on information received from the server, referencing a knowledge base. The knowledge base is a dynamic information infrastructure that is updated to reflect past conversation history and user characteristics.

[0642] The generated response is optimized by the server and sent to the user's device. This allows the user to quickly resolve digital issues based on the provided information.

[0643] Furthermore, this system can share information with external devices as needed and collaborate in real time. For example, it can send notifications to the devices of family members or support providers to offer additional assistance.

[0644] A concrete example of a prompt would be, "The user wants to know how to install a new app. Please explain the steps in detail." When this prompt is passed to the AI ​​generation model, it can provide a more appropriate response.

[0645] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0646] Step 1:

[0647] The user uses a control device to input questions about digital usage. For example, they might input a question like, "I want to know how to install a new app," and send it from the device to the server. The input data is text information about the user's specific problem. The output is the transfer of the user's question to the server.

[0648] Step 2:

[0649] The server receives text information sent from the user's terminal. This information is then passed to the natural language processing engine. The input is raw text data from the user, and the output is structured information for analysis. The server uses the natural language processing engine to analyze the sentence structure and extract keywords. Specifically, this involves morphological analysis of the text and identifying important keywords.

[0650] Step 3:

[0651] The server uses information parsed by the natural language processing engine to formulate prompts for the generative AI model. For example, it might generate a prompt such as, "The user wants to know how to install a new app. Please provide specific instructions." The input is structured parsed information, and the output is a query to the generative AI model.

[0652] Step 4:

[0653] The generative AI model generates the optimal answer by referencing a knowledge base based on prompts received from the server. The input is the prompt text and related knowledge base information, and the output is a specific and personalized answer. It prepares specific answers such as "The download procedure for the XX app is as follows." The generative AI model further refines the answer by taking user characteristics information and past conversation history into consideration.

[0654] Step 5:

[0655] The server reviews the generated responses and prepares them for transmission to the user. The input is the response from the generative AI model, and the output is data formatted for final transmission to the user's terminal. The server removes unnecessary information and adjusts the writing style to an appropriate form.

[0656] Step 6:

[0657] The user receives the final response sent from the server via their control device. Based on this, the user performs the necessary actions on their digital device. The input is the customized response sent from the server, and the output is the user's specific actions and problem-solving. The user uses the information provided by the server to accurately execute the steps to install the application.

[0658] (Application Example 1)

[0659] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In modern digital purchasing platforms, users face challenges in quickly obtaining detailed product information and receiving personalized online purchasing assistance. This can lead to users spending a significant amount of time trying to find the information they need, potentially resulting in lower satisfaction.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0662] In this invention, the server includes means for receiving and analyzing data input from a user information processing device, means for generating a response using a computational AI model based on the analyzed data, and means for personalizing the generated response and transmitting it to the user information processing device. This enables the rapid provision of product information and procedures requested by users at a virtual shopping location.

[0663] A "user information processing device" is a hardware or software device used by users to input data, and includes smartphones and smart glasses.

[0664] "Means for receiving and analyzing data" refers to technical elements that receive data transmitted from a user information processing device and perform procedures to understand its contents.

[0665] A "computational AI model" is an artificial intelligence system used to generate appropriate responses based on input data from a user.

[0666] "Means for personalizing and sending responses" refers to the process of adjusting the generated response based on the characteristics and requests of individual users and sending it to a user information processing device.

[0667] "Interaction history" refers to a record of past interactions between users and the system, and is used as data to improve the user experience in the future.

[0668] A "knowledge base" is a database that a system uses as a reference when providing information, and it is constantly updated to contain the latest information.

[0669] A "virtual shopping location" refers to a digital platform where users can search for and purchase products online, providing them with a virtual shopping experience.

[0670] The system that implements this application example uses a user information processing device, a server, and a computational AI model. Users visit a virtual shopping location using a user information processing device such as a smartphone or smart glasses and input questions and requests. The data entered by the user is sent from the device to the server.

[0671] Upon receiving input data, the server uses language processing techniques to analyze the sentence structure and intent. This analysis utilizes natural language processing engines such as SpaCy and NLTK. Based on the analysis results, the server invokes a computational AI model to generate a response optimized for the user's questions and requests. Examples of such AI models include OpenAI GPT and Google BERT.

[0672] The generated responses are personalized on the server and sent to the user information processing unit. This process also considers past interaction history and user characteristics, and updates the knowledge base. As a result, users can receive personalized assistance in real time, including detailed product information and purchasing procedures, within the virtual shopping environment.

[0673] As a concrete example, consider a scenario where a user is searching for a new coat on a fashion sales platform using smart glasses. The user asks, "Does this coat come in other colors?" The AI ​​instantly provides multiple color options, simplifying the purchase process.

[0674] Examples of prompt messages include, "Do you have this coat in a different color?"

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] Users input questions and requests regarding the products they wish to purchase via text or voice through their smartphone or smart glasses. The input data is sent to the server as prompt messages. In this process, the user's questions are used as input, and prompt messages are sent to the server as output.

[0678] Step 2:

[0679] The server passes the received prompt sentence to a natural language processing engine (e.g., SpaCy, NLTK) to analyze its structure and intent. This process uses the prompt sentence as input and generates semantic information for words and phrases as output. Specifically, it performs tasks such as keyword extraction and contextual interpretation.

[0680] Step 3:

[0681] The server calls a computational AI model (e.g., OpenAI GPT, Google BERT) based on the analysis results to generate an optimized response to the user's question. In this process, the analysis results are used as input, and the generated response is obtained as output. The AI ​​uses the knowledge it has learned from the training data to provide meaningful information to the user.

[0682] Step 4:

[0683] The server considers the user's past interaction history and profile information, referencing a knowledge base to personalize the generated response. This process uses historical data as input for the purpose of personalizing the response. The output is a custom response tailored to the user's request.

[0684] Step 5:

[0685] The server sends a tailored response to the user's device and provides it to the user through display or audio output. This process uses a custom response as input and displays the response on the user's device as output. Specifically, this might involve displaying the response on the screen or having a voice assistant read the response aloud.

[0686] Step 6:

[0687] If necessary, the server notifies relevant devices of the relevant information. This process allows for the sharing of information with external parties, with the user's permission. Response data is used as input, and notifications are sent to external devices as output.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention relates to a system that, when a user engages in emotionally charged interactions, uses an emotion engine to recognize the user's emotions in real time, and then utilizes a generative AI model to generate and adjust responses based on those emotions. This system mainly consists of a user terminal, a server, an emotion engine, and a generative AI model.

[0690] Users use their devices to ask questions about digital support. In these cases, the user's text input may include emotional elements. For example, they might type, "I'm having a lot of trouble installing the new app."

[0691] The device sends this message to the server. The server analyzes the received message, using a natural language processing engine to understand the sentence structure and intent, and simultaneously using an emotion engine to recognize the user's emotions. Here, for example, the emotion "anxiety" might be detected.

[0692] The server provides recognized emotion information to a generating AI model, which then generates a more appropriate response. The response is adjusted to a tone that takes the recognized emotion into consideration. For example, a gentle response such as, "Don't worry. Let's work together to resolve the app installation issue," might be generated.

[0693] The generated responses are further personalized and sent to the user's device in an appropriate format. The user receives these responses on their device and can solve the problem by following the specific instructions. Additionally, the results of the emotion engine are recorded in a knowledge base, and changes in the user's emotions are tracked.

[0694] This system allows users to seamlessly and safely utilize digital technologies while also receiving emotional support.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The user launches the chat app on their device and enters their question or the type of support they need. For example, they might type, "I'm having trouble downloading the app. What should I do?" Sending the message initiates communication with the server.

[0698] Step 2:

[0699] The terminal packets the user's input message, encrypts it, and then sends it to the server. This ensures that the data arrives at the server in a secure state.

[0700] Step 3:

[0701] The server sends the received message to a natural language processing engine for analysis. The engine extracts important keywords and their context from the text to understand the intent of the user's question.

[0702] Step 4:

[0703] At the same time, the server uses an emotion engine to recognize the user's emotions from the received messages. For example, it can detect emotions such as "anxiety" or "impatience" from the expression "I'm in trouble."

[0704] Step 5:

[0705] The server provides the analyzed intent and recognized emotion information to the generating AI model. The model then generates a response based on this information, adjusting the tone and content of the response based on the emotion.

[0706] Step 6:

[0707] The generated responses are further personalized based on the user's past conversation history and profile, and then sent from the server to the user's terminal in a finalized form.

[0708] Step 7:

[0709] The user reviews the response received on their device and takes action based on it. Specifically, they are given concrete instructions such as, "Access the app store, search for the app again, and try downloading it."

[0710] Step 8:

[0711] The server records the results of this interaction and updates the knowledge base with the content of the conversation and sentiment history. This will enable more accurate support for future use.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] Conventional information processing systems struggled to generate responses that took user emotions into account, resulting in an inadequate user experience. Furthermore, they lacked mechanisms for effectively generating personalized responses that reflected emotions and for updating information during that process. This limited the services available to users, making it difficult to address individual needs.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes means for receiving and analyzing data input from a user device, means for recognizing emotions using an information processing engine based on the analyzed data, and means for generating responses using a generative AI algorithm based on the emotions. This enables the automatic generation of personalized responses that reflect the user's emotions and the dynamic updating of knowledge accumulation.

[0717] A "user device" is an electronic device used by a user to input and receive information.

[0718] "Data" refers to the information and messages entered by users, which are the subject of analysis.

[0719] "Means of analysis" refers to a system that has the function of analyzing received data and understanding its content and structure.

[0720] An "information processing engine" is a program that analyzes the content of data and processes it using specific algorithms and methods.

[0721] "Means of recognizing emotions" refers to methods of extracting and understanding emotional elements from user input data.

[0722] A "generative AI algorithm" is an artificial intelligence technology that generates responses based on specified conditions.

[0723] "Means for distributing responses" refers to a system that has the function of sending and displaying the generated responses to the user's device.

[0724] "Knowledge accumulation" is a technology that stores past information and history and updates it as needed.

[0725] The embodiments for carrying out this invention are shown below.

[0726] Users use a device to input specific information, for example, when they need to inquire about or receive support regarding a digital service. This device, such as a computer or smartphone, has the functionality to send the data entered by the user to a server.

[0727] Upon receiving this input data, the server first analyzes the information using natural language processing. Specifically, natural language processing engines such as SpaCy and NLTK are used. Through this engine, the server analyzes the grammatical structure and intent of the data. At the same time, it uses an emotion engine to recognize the user's emotions. For example, IBM Watson Tone Analyzer or similar emotion recognition tools may be used.

[0728] Based on this analysis, the server uses a generative AI model to generate an appropriate response. Here, GPT-3 or a similar generative AI algorithm is used. This AI model is capable of constructing gentle, specific responses that take user emotions into account in response to user inquiries. An example of a prompt might be, "If the user is confused, create a reassuring suggestion."

[0729] The generated response is further individualized and sent from the server to the user's terminal. The user receives this response on their terminal and can then take action to resolve the specific problem.

[0730] Furthermore, the server records the dialogue history and sentiment analysis results in a knowledge base, which will be used to improve future interactions. This system allows users to receive seamless support that is sensitive to their emotions.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] Users provide input regarding digital support through their device. This input data is in text format and includes inquiries and requests, such as "I'm having trouble installing a new app." The device receives this input and prepares to send it to the server.

[0734] Step 2:

[0735] The server receives text data from the terminal to be analyzed. First, it uses a natural language processing engine to analyze the grammatical structure and intent of the data. Tools used here include SpaCy and NLTK. Through this analysis, the server clarifies the basic content of the user's inquiry. The output of this step is the grammatically structured data and the interpretation of its intent.

[0736] Step 3:

[0737] The server passes the analyzed information to the emotion engine. The emotion engine uses tools such as IBM Watson Tone Analyzer to extract emotional elements from the text. Here, emotions such as "anxiety" and "confusion" are identified. This emotional information is the main output of this step.

[0738] Step 4:

[0739] The server uses the identified emotion information as a prompt based on a generative AI model. Specifically, the generative AI model is given a prompt such as "How to reassure the user if they are feeling anxious?" The generative AI model (e.g., GPT-3) receives this prompt and generates an emotion-sensitive response. The output is a polite response message that takes the user's emotions into consideration.

[0740] Step 5:

[0741] The generated response is further adjusted by the server to suit the individual user's situation. Past conversation history and user profile are referenced to optimize the answer. The output at this stage is the personalized final response.

[0742] Step 6:

[0743] The server sends the finalized response to the user's terminal. The terminal displays this response to the user. This response provides the user with instructions on what specific action to take. The output of this step is the response received by the user.

[0744] Step 7:

[0745] The server records sentiment information and dialogue results obtained during the response generation process in a knowledge base. This enables more personalized responses in future interactions. The output of this step is the updated knowledge base information.

[0746] (Application Example 2)

[0747] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] To improve the customer service experience in physical stores, there is a need for systems that can grasp customers' emotions in real time and provide appropriate responses according to the situation. However, conventional technologies have not adequately integrated emotion recognition and response generation in customer interactions, resulting in inconsistent service quality and a lack of improvement in customer satisfaction.

[0749] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0750] In this invention, the server includes means for receiving and analyzing information input from a user terminal, means for generating a response using a generation AI model based on the analyzed information, means for individualizing the generated response and transmitting it to the user terminal, means for updating a knowledge database considering past dialogue history and user attributes, means for providing emotional information in real time via an external device, and means for adjusting the content and tone of the response based on the emotional information. This enables staff in physical stores to recognize customers' emotions in real time and provide appropriate and individualized responses accordingly.

[0751] A "user terminal" refers to a device used by a user to input or output information, and includes smart glasses and personal digital assistants (PDAs).

[0752] "Means of analysis" refers to methods and devices for analyzing information received from a user terminal and understanding its structure and intent.

[0753] A "generative AI model" is an artificial intelligence technology model that generates natural language output based on input data.

[0754] "Means of personalized transmission" refers to methods or devices that customize generated responses according to the user's attributes and circumstances and transmit them to the user's terminal.

[0755] A "knowledge database" refers to a collection of information that a system uses to store and learn from past interactions and user data.

[0756] "Means of providing emotional information in real time via external devices" refers to methods or devices that instantly inform users of their emotional state through external devices such as smart glasses.

[0757] "Means of adjusting the content and tone of responses based on emotional information" refers to methods and devices for optimizing the wording and expression of responses according to recognized emotions.

[0758] The system that realizes this invention uses a user terminal, a server, and an external device for recognizing emotions. Smart glasses are used as the user terminal, providing an environment in which the user can easily input information and receive responses.

[0759] First, when a user provides voice input through smart glasses, that information is sent to a server. The server analyzes this voice data and converts it into text data using a speech recognition system. Next, a natural language processing engine is used to analyze the structure and intent of the text. Furthermore, an emotion engine is utilized to recognize the user's emotions in real time and understand the customer's emotional state.

[0760] The recognized emotion information and text data are input into a generative AI model, which generates an appropriate response. The generative AI model adjusts the tone of the response, taking into account the input emotion, enabling more personalized communication. For example, if a user says, "I really like using this product," the server will generate a positive response such as, "Thank you, I'm glad you're satisfied."

[0761] The generated responses are displayed in a personalized format on the user's smart glasses. Furthermore, the dialogue history and user attribute information are stored in a knowledge database and used for future system improvements.

[0762] An example of a prompt message would be: "Based on the customer's statement, 'This bouquet of roses is very beautiful,' generate a positive response that takes into account their joy and interest." This system enables staff to provide high-quality customer service based on customer emotions.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The user provides voice input through smart glasses. The voice data is captured by the device and transferred to the server. The voice data becomes input data for post-processing.

[0766] Step 2:

[0767] The server uses a speech recognition system to convert received audio data into text data. It analyzes the audio signal as input and outputs text as character information. This data is used for natural language processing.

[0768] Step 3:

[0769] The server uses a natural language processing engine to analyze the sentence structure and user intent of the text data. It extracts grammatical structure and keywords from the input text to understand the user's intent. This result is then used for sentiment analysis.

[0770] Step 4:

[0771] The server recognizes the user's emotions in real time from text through its emotion engine. Using the parsed text as input, it outputs the emotional state. This information is useful for generating responses.

[0772] Step 5:

[0773] The server inputs emotional information and text data into a generative AI model and generates an appropriate response. It creates prompt sentences that take emotions into account, and the model outputs a more natural response. This response is then delivered to the user.

[0774] Step 6:

[0775] The generated response is personalized on the server and sent to the user's smart glasses. It is displayed as a customized message, enhancing the user's interaction experience.

[0776] Step 7:

[0777] Dialogue history and user attribute information are stored in a knowledge database by the server. This generates data that can be used to improve the system in the future, enabling more accurate services.

[0778] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0779] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0781] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0782] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0783] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0784] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0785] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0786] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0787] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0788] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0789] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0790] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0791] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0792] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0793] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0794] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0795] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0796] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0797] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0798] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A means for receiving and analyzing information entered from a user terminal,

[0802] A means for generating an answer using a generative AI model based on the analyzed information,

[0803] A means for individualizing the generated response and sending it to the user terminal,

[0804] A means of updating the knowledge base considering past conversation history and user profiles,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, comprising means for analyzing the structure and intent of a sentence using natural language processing when information is received from a user terminal.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising means for sending a generated response to the user and simultaneously notifying an external device of related information.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A means for receiving and analyzing information input from a user's operating device,

[0813] A means for generating an answer using a generative AI model based on the analyzed information,

[0814] Means for individualizing the generated response and transmitting it to the user's operating device,

[0815] A means of updating the knowledge base by taking into account past dialogue history and user characteristic information,

[0816] A means of notifying relevant information in order to share information with external devices,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, comprising means for analyzing the structure and intent of a sentence using natural language processing when information is received from a user's operating device.

[0820] (Claim 3)

[0821] The system according to claim 1, comprising means for sending a generated response to a user and simultaneously notifying an external device of related information.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] A means for receiving and analyzing data input from a user information processing device,

[0825] A means for generating a response using a computational AI model based on the analyzed data,

[0826] Means for personalizing the generated response and transmitting it to a user information processing device,

[0827] A means of updating the knowledge base considering past interaction history and user characteristics,

[0828] A means of providing immediate support at a virtual purchase location through a user interface,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, comprising means for analyzing the structure and intent of a sentence using language processing technology when data is received from a user information processing device.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising means for transmitting a generated response to a user and simultaneously notifying a related device of related information.

[0834] "Example 2 of combining an emotion engine"

[0835] (Claim 1)

[0836] A means for receiving and analyzing data input from user devices,

[0837] A means for recognizing emotions using an information processing engine based on the analyzed data,

[0838] A means for generating a response using a generative AI algorithm based on the aforementioned emotions,

[0839] A means for individualizing the generated responses and distributing them to user devices,

[0840] A means of updating the knowledge base by taking into account past information history and user information,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising means for analyzing the structure and intent of a sentence using automatic language analysis when data is received from a user device.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising means for sending a generated response to a user and simultaneously notifying an external device of related information.

[0846] "Application example 2 of combining emotional engines"

[0847] (Claim 1)

[0848] A means for receiving and analyzing information entered from a user terminal,

[0849] A means for generating an answer using a generative AI model based on the analyzed information,

[0850] A means for individualizing the generated response and sending it to the user terminal,

[0851] A means of updating the knowledge database considering past dialogue history and user attributes,

[0852] A means of providing emotional information in real time via an external device,

[0853] A means of adjusting the content and tone of the response based on the aforementioned emotional information,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, comprising means for analyzing the structure and intent of a sentence using natural language processing when information is received from a user terminal.

[0857] (Claim 3)

[0858] The system according to claim 1, comprising means for sending a generated response to a user and simultaneously notifying an external device of related information. [Explanation of symbols]

[0859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving and analyzing information entered from a user terminal, A means for generating an answer using a generative AI model based on the analyzed information, A means for individualizing the generated response and sending it to the user terminal, A means of updating the knowledge base considering past conversation history and user profiles, A system that includes this.

2. The system according to claim 1, which includes means for analyzing the structure and intent of a sentence using natural language processing when information is received from a user terminal.

3. The system according to claim 1, comprising means for sending a generated response to the user and simultaneously notifying an external device of related information.

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